Abstract 5362: AI-enabled novel workflow to evaluate T-cell activity <i>in vitro</i> using 3D spheroid models
Authors: Zhisong Tong, Oksana Sirenko, Misha Bashkurov, Angeline Lim
Journal: Cancer Research (2023)

Abstract T-cell therapies are designed to help our immune system eliminate cancer cells. Those include CAR T-cells (Chimeric Antigen Receptor engineered T-cells), tumor infiltrating lymphocytes (TIL), and other genetically modified T-cells. In recent years, the field of cell therapy has started to expand, including the launch of the first CAR T-cell therapies to treat blood cancer in 2017, which was a critical milestone in this field. Despite its boom, the discovery of novel immunotherapies that specifically enhance T-cell response against cancer cells remains a challenging task, limited by the absence of robust in vitro models to evaluate these immunotherapies throughout their development. The use of CAR T-cells on solid tumors has been lagging due to challenges that include tumor heterogeneity, immunosuppressive microenvironments, and the lack of unique tumor antigens that can be recognized by the CAR-T cells. As such, the ability to screen for CAR T-cells (e.g. with CRISPR) that effectively target and kill tumors is an area of active research. Here we describe a method for assessment of T-cell effect and penetration into the multi-cellular 3D tumor spheroids as a proof-of-concept model for in vitro CAR T assays. Spheroids were formed from Hela cell lines in 96-well round bottom plates. We activated the human peripheral blood mononuclear (PBMC) cells with PMA/ionomycin for 6 hours, then treated the spheroids with stimulated PBMC for 72 hours. Samples were imaged with a confocal imager every 2 hours for a 72 hour period. High-content imaging and analysis allowed us to observe and measure phenotypic changes in cancer spheroids and the process of T-cell penetration over a period of 72 hours. To optimize the workflow, we developed an image analysis approach that uses a deep learning-based segmentation model and a machine learning-based classification model to quantify the T-cell induced phenotypic changes in the spheroids using brightfield images. Our results show clear distinct phenotypic changes among different treatment groups and the feasibility of using AI-based analysis workflows to accurately predict the efficacy of T-cells in an in vitro assay. Moreover, the information associated with the penetration of T-cell into the 3D spheroids was explored by calculating the coordinates of the penetrated T-cells and T-cell movements from the nearest edge of the spheroids where the T-cell penetration speed was estimated. Our results show that the stimulated T-cells have much greater penetration speed compared to non-stimulated T-cells. Image analysis also allowed us to measure and quantitate the deterioration of cancer spheroids and changes in their morphology. Overall, our results show that the 3D spheroid models and the high-content analysis workflow may potentially be used as a metric to evaluate the efficacy of cell therapy in vitro. Citation Format: Zhisong Tong, Oksana Sirenko, Misha Bashkurov, Angeline Lim. AI-enabled novel workflow to evaluate T-cell activity in vitro using 3D spheroid models. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5362.